In general, data mining is an information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis.
The Web Services Description Language (“WSDL”) and Extensible Markup Language (“XML”) have emerged as standard client/server protocols. Similarly, standards are emerging for data mining (“DM”) and web services. Specifically, OLE DB-DM defines extensions to the Structured Query Language (“SQL”) for the creation of predictive models (i.e., “DM-SQL”) and the XML for Analysis standard has been proposed as a way of transporting DM-SQL via a Web Service.
Now, existing methods and systems allow for the creation of data mining models on a batch basis or on an incremental basis by adding new, previously unused rows. For example, commercial products typically implement interactive decision trees using single tier and client/server architectures using the Distributed Component Object Model/Component Object Model (“DCOM/COM”). However, such existing systems do not provide effective user interaction or model visualization in client/server systems.
A need therefore exists for an improved method and system for interacting with and visualizing data mining models in client/server systems. Accordingly, a solution that addresses, at least in part, the above and other shortcomings is desired.